Bibliographic record
Abstract
How common are resorts to ‘common sense’ in Charter decision making at the Supreme Court of Canada? It is argued that the Court’s capacity to name reality for everyone is a powerful one and so becomes a site of competition among rival conceptions of common sense. This competition over naming common sense was in operation in the Supreme Court rulings of Gosselin and Chaoulli. In the first case, the Court declared that discriminating against young people in the provision of welfare at less than subsistence levels was supported by common sense. In the second case, a dispute concerning a provincial ban on private health insurance, members of the Court acknowledged the possibility that there could be more than one version of what constitutes common sense. This had the perverse effect, however, of equating empirical evidence offered by Quebec with conjecture offered by the claimants to overturn the ban. The Court also has had recourse to common sense in the course of its proportionality inquiry under section 1. Alive to the legitimacy problems associated with resorting to common sense in order to invalidate legislation, the Court, in these cases, has preferred to rest its proportionality analysis on the seemingly more rigorous and democracy-promoting least restrictive means requirement. Common sense, in this way, serves less controversial purposes but nevertheless underscores the power of the Court to declare what is conventional wisdom for all.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.037 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.019 | 0.096 |
| Scholarly communication | 0.022 | 0.013 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".